9 WISTL 14, Deuxième édition Casablanca, Novembre gestion du dernier kilomètre. Kenza Oufaska, Ibrahim Boubia and Anas Kartobi, "Optimization of the production of renewable energy from wind turbine systems". Abstract : Wind generation of electricity is a logistics business that has a major economic and social impact. In Morocco, there are only insignificant and inadequate resources used to support economic development that the Kingdom has experienced lately. This work is to develop a method for optimal management of intermittent aspects wind. Initially, a modeling of the various characteristics of the components of the problem is raised. Next, using the Mixed Linear Programming, a mathematical formulation of management system is performed, and finally, implementation of operating policies for the definition of a strategy to follow is given. Keywords : Power generation, Wind generation, Optimization, Storage. Kenza Oufaska, El Mamoun Nihale, Mrabet Hind et Ouiame Laridi, "Transport logistique". Abstract : Les frais encourus par les entreprises pour garantir la livraison des marchandises au bon endroit et au bon moment ne cessent d augmenter en raison de l encombrement des infrastructures et des prix élevés du carburant. De plus, les distances s étendent jour après jour. Cela exige des sociétés à payer des coûts énormes et souvent doubles (aller-retour) afin d assurer le service auprès des clients. Le problème de transport logistique consiste à construire une collaboration entre les entreprises impliquées dans un réseau de transport pour réduire les coûts de transport tout en respectant les délais accordés par chacun des collaborateurs. 15H00 16H30 COMMUNICATIONS ORALES Abdelaziz EL Kfita et Omar Drissi-Kaitouni, "Survey of vehicle fleet maintenance optimization : past and future trends". Abstract : Vehicle Fleet Maintenance management represents an important activity at tactical and operational levels to be faced by managers of fleet within private companies and public agen-cies devoted to passengers and freight transportation services, given the importance of fleet availability and reliability to keep a good operation and branding of the company or agency via its service. Mathematical models, computation techniques, opera-tional research tools and technology have been developed for optimizing the maintenance cost of fleets in order to serve the customers demand with the objective of cost efficiency. The aim of this paper is to identify the most relevant problems in fleet maintenance management relative to transit and freight transport modes, and to present an overview of past and recent emerging trends. Asmaa Elmortada, Ahmed Mokhlis, Said Elfezazi, "The influence of HR practices on supply chain". Abstract : In an economic environment increasingly complex and competitive, Moroccan enterprises, including industrial activity, are now aware, more than ever, of the importance of controlling the human resources function as a primary factor to the success and performance of their supply chains. The concern of this article is to highlight the role of HR function in the success of the supply chain, in the light of the culture of the company, and under the effect of HRM practices influenced by the competitive environment, and the resulting mutations. B. Bouziyane, M. Cherkaoui, B. Dkhissi, "A hybrid genetic algorithm to solve the Vehicle Routing Problem with Time Windows static and dynamic". Abstract :In this paper we are interested in vehicle routing optimization which is an important problem in the fields of transportation. Our object is to design a tool for VRPTW(vehicle routing problem with time windows) by the hybridization of the genetic algorithm method and the variable

13 WISTL 14, Deuxième édition Casablanca, Novembre Abstract : This paper presents the solution of the problem of vehicles storage on the vertical storage space in Terminal Parking of Marsa Morocco. An operations research modeling was developed, it aims to schedule the proposal for the allocation of vehicles. To automate the process and keep tracking the flow of vehicles, we have developed two software that communicate with RFID technology. In this work, we conduct two studies to illustrate the impact of RFID technology in the management of information systems and conception packages fleet management systems.

22 Optimization of inserting a new flight in airline schedule using evolutionary algorithms Hicham Rahil, Badr Abou El Majd, Mohamed Bouchoum Computer Science and Decision Aid Laboratory, Hassan II University, Casablanca Abstract In this work, we introduce the quality of service index (QSI), as a duel parameter for the profitability of a new opened route. This decision-making aspect would be expressed by the repercussions test of the new insertion in a pre-set flight Schedule, taking in count the generated delays and then see the impact of this decision on the quality of service offered to a target customers. These delays generated as well as other costs will be the subject of a multi-objective optimization with financial earn through the insertion of this new market. The abstract goes here. Keywords : NSGA II algorithm, Pareto optimal, Outbound/Inbound connection, Hub and spokes, multiobjective optimization, flight schedule. 1 Introduction The construction of an airline schedule requires numerous mutually interacting decisions to be made that influence the profitability of the schedule and its operational performance.the final schedule will impose numerous requirements on the airline s operating environment,including airspace, airport, runways and maintenance facilities. Moreover, it could significantly influence the operations of other airlines, especially in case the selected schedule has a hub-and-spoke structure.the operational performance of an airline schedule is the result of a complex interaction between the schedule and its operating environment [1]. 2 Problem Formulation 2.1 Multi-objective problem In many real-world optimization applications, the decision maker is always faced with the presence of multiple incommensurable and often competing objectives. The solutions for the multi-objective problem (MOP) often result from both the optimization and decision making process. When trying to solve an MOP, a set of trade-off solutions is the target of the solution algorithm and the one that will be chosen depends on the needs of the decision maker. An MOP can be defined as : (1) min x f i (x) (i = 1,..., N) s.t g j (x) 0 (i = 1,..., M) x l x x u where (f i ) N i=1 are the objective functions, x is the design variable vector, x l and x u are respectively the lower and upper bounds of x,and the functions (g i ) M i=1 are the constraints. The optimization problem (1) has generally more than one solution which satisfy the trade-offs among the N objective functions.the set of those solutions is called the Pareto set [2]. 2.2 Model and notations Let A k the set of the weekly arrivals time from an airport k K to the hub, and we will mention a ki the ith one with i {1, 2,.., N k }. The a ki are classed in ascending order.n k is the weekly frequency of the line between the hub and an airport k K.The same applies for D k, the set of weekly departures time, where d kj is the jth one, i {1, 2,.., N k } and the d kj are also classed in ascending order. Arrivals and departures time are integers and they are all included in the interval [0, 10080] minutes (a week). 1

23 P k the potential of the targeted airport k K, means the number of transported passenger per year, between airport k and the new opening, via all possible air routes. The new opened line is characterized by it s hub departure time x, and the arrival time x+t, where t is the round trip duration. Let be t min and t max, the minimal and maximal allowed stop time, at the hub.these parameters are defined by the airliner. We will take the minute as unit of time : T (j, h, m) = m + 60 h + (j 1) Our problem can be formulated as a multi-objective problem by using two objective functions f 1 and f 2. The first one aims to increase the financial earn through the insertion of this new market using P k, the passenger potentials recorded for an airport k, as a weighting in the objective function, and subsequently,the new market will encourage the connection of airports with high potentials. The second one consists to improve the quality of service index (QSI) affected by disruptions, delays, and it s related costs. and { 1 Inbound connection is realized for dkj Y kj = 0 otherwise Outbound connection is realized when a given value a ki comes inside the arrival time window [x t max, x t min ], and the inbound one is realized when a given value d kj comes inside departure time window [x + t + t min, x + t + t max ], where a ki and d kj are respectively the sets of weekly hub arrivals and departures to the targeted Airport k (figure 1) Flight profitability Network planning is an integral part of every airline s revenue generation capabilities. Considering today s challenging business environment, one way of knowing whether an airline s network planning and scheduling is paying off is to see how much revenue is improving. There are several aspects of scheduling that can reduce operating costs and lift overall profitability. Be it for fleet optimisation or assessing potential profitability of different forecasted routes, planning and scheduling can have a significant impact on meeting demand, while also making the most of connections or maximising revenues. In addition, the team must work in conjunction with several departments, including revenue management, to ensure there is same level of understanding of the target customers in a given market. Our first objective function f 1 will be to maximize the profitability of the new opening by choosing the best departure time x, we will use two decision variables Y ki and Y kj, w k the P k percentage of the total P airport passenger potentials, w k = k Nk where N k is i=1 Pi the number of airports subject to connection. w k will be used as weight vectors in the objective function f 1. f 1 will be written as follows f 1 (x) = kij w k Y ki Y kj where { 1 Outbound connection is realized for aki Y ki = 0 otherwise figure 1 : Hub Outbound/Inbound connections Quality of Service Index This aspect could be approached from various points of view. We will take the generated delays as quality of service indicator, that means, we have to decrease the generated delays,result of the precedent correspondence. The total of all generated delays can take the following forme f 2 (x) = ki (x a ki ) Y ki + kj (d kj (x + t)) Y kj the model insures that we include only delays generated by connected airports, via the decision variables Y ki and Y kj. Y ki {0, 1} Y kj {0, 1} 3 Numerical results 3.1 Evolutionary algorithm jmetal stands for Metaheuristic Algorithms in Java, and it is an object-oriented Java-based framework for multiobjective optimization with metaheuristic techniques [3]. Under jmetal, a metaheuristic is composed of a class defining the algorithm itself and another class to execute it. 2

24 This second class is used to specify the problem to solve, the operators to apply, the parameters of the algorithm, and whatever other parameters need to be set.in our paper the two classes used are NSGAII and NGAII main, respectively [4]. figure-3 : Non-convex front using NSGA-II figure-2 : Schematic of NSGA-II algorithm 3.2 Pareto optimal Experimentation Default setting population Size = 1000 ; maxevaluations = 1000 ; mutation Probability = 1.0 ; crossover Probability = 0.9 ; mutation Distribution Index = 20.0 ; crossover Distribution Index = 20.0 ; If we want to modify these default settings, we have to do it by changing them in the files defining the problem. We execute a number of independent runs than we analyze the results Pareto Front An example of weak and strict Pareto optima is shown in figure-3 : outside points p1 and p5 are weak Pareto optima ; points p2, p3 and p4 are strict Pareto optima. these five non dominated points, corresponds to 5 departure times x1, x2, x3, x4 and x5 to the new destination, guarantying a maximum of profitability and minimising the delays average on the hub. the objective of the multiobjective optimization is to give the best choice to the decision maker, moreover, as a frequency of the projected flight, we can choose more optimal results. 4 Conclusion We have introduced a new multi-objective optimization approach for the insertion of new flights in the airline schedule problem. The implementation was carried out using the jmetal Framework. The pareto set is generated using NSGA-II Algorithm. Numerical results based on real instance of Royal Air Maroc schedule in the hub of Casablanca, demonstrates the efficiency of this work, and as perspective, we include the disruption model in order to construct a robustness approach. Références [1] Edmund K. Burke, Patrick De Causmaecker, Geert De Maere, A multi-objective approach for robust airline scheduling Computers and Operations Research, Volume 37, Issue 5, May [2] Kalyanmoy Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley and Sons, 5 juil pages. [3] Juan J. Durillo, Antonio J. Nebro, jmetal : A Java framework for multi-objective optimization, Advances in Engineering Software, Volume 42, Issue 10, October 2011, Pages [4] Kalyanmoy Deb, Associate Member, IEEE, Amrit Pratap, Sameer Agarwal, and T. Meyarivan, A Fast and Elitist Multiobjective Genetic Algorithm :NSGA- II, IEEE Transactions on evolutionary computation, Volume. 6, No. 2, April

26 A hybrid genetic algorithm to solve the Vehicle Routing Problem with Time Windows static and dynamic B.Bouziyane, M.Cherkaoui, B.Dkhissi Department of Mathematics ENSA, Tetouan, Morocco Abstract In this paper we are interested in vehicle routing optimization which is an important problem in the fields of transportation. Our object is to design a tool for VRPTW(vehicle routing problem with time windows) by the hybridization of the genetic algorithm method and the variable neighborhood search method. This algorithm reduces the transportation costs by using a fleet of vehicles, and improves the quality of service by reducing the delay time and the waiting time for each client, in both cases: static and dynamic. Keywords Optimization; Vehicle Routing Problem (VRP); hybridization ;Genetic Algorithm; I. INTRODUCTION The VRP arises naturally as a central problem in the fields of transportation, distribution, and logistics [4]. The Vehicle Routing Problem with Time Windows (VRPTW) is an extension of the classic Vehicle Routing Problem (VRP) and is defined as the problem of minimizing time and costs in case a fleet of vehicles has to distribute goods from a depot to a set of customers. The VRPTW is one of the most important variants of the VRP that has arisen due to the growing importance of time constraints in the modern societies. Time windows constraints are indeed common in many applications, including bank deliveries, postal deliveries, grocery distribution, dial-a-ride service, bus routing, and repairmen scheduling. In this article, we propose a hybrid genetic algorithm to minimizing the tradeoff between the sum of the delays, the waiting time and the total transportation cost. This technique can potentially yield near-optimal solutions to many difficult optimization problems. A hybrid optimization method that combines the genetic algorithm with the Variable Neighborhood Search (VNS) is proposed to treat the two version of the VRPTW: static and dynamic. This is a multicriteria vehicle routing problem, which is modeled as a single criterion problem, this by using weights that reflect the importance of each criterion. All problem parameters, such as demand locations and time windows, are assumed to be known with certainty. II. BRIEF LITERATURE REVIEW Due to its complexities and usefulness in real life, the VRPTW continue to draw attention from researchers and has been a well-known problem in network optimization. The first exact method capable of solving instances of interesting size of VRPTW was proposed by Desrochers et al(1992)[5]. Early surveys of solution methods for VRPTW can also be founded in Golden and Assad [6, 7] and Solomon and Desrosiers [10]. The most recent methods attempt to combine multiple metaheuristics,such as the Method based on tabu search and proposed by Homberger & Gehring(2005).In this method,the problem is solved in two phases : minimize the numbre of vehicles used and the total distance traveled by the vehicles.the recent developments can also be found in Ioannou, Kritikos, and Prastacos [9], Br aysy and Gendreau [2, 3], and Br aysy, Dullaert, and Genderau [1]. Olli Br aysy& Gendreau(2004) conclude that the most effective methods are currently evolutionary algorithms, hybridized with local search methods, such as tabu search. III. PROBLEM FORMULATION The main goal is to serve all customer requests by minimizing: The transportation cost by reducing the total travelled distance, and the total sum of delay time and stopping time. A. Assumptions and parameters Before presenting the model of VRPTW, we will clarify some assumptions, and identify the parameters that characterize this problem: Each customer is served exactly once by one vehicle. With only one depot, each vehicle starts from the depot and must return there after visiting the last customer. The vehicles have the same capacity, and this capacity constraint must be respected. This problem is considered with flexible time windows: it is allowed to arrive after the time window has closed.

27 If the vehicle arrives too early it has to wait until the window opens. N : The set of all clients and depot K : The number of vehicles d ij : Euclidean distance between client i and client j v : The speed of a vehicle t : The travel time from customer i to customer j ij dij tij v e i : The earliest arrival time for customer i. l i : The latest arrival time for customer i. s i : The service time for customer i. q i : The demand for customer i. Q : The vehicle capacity. C : The transportation cost per unit distance. : Weight of each component of the objective function i A The vehicle arrival time at customer i. i D = The vehicle departs from customer i. i y = The goods quantity in the vehicle K visiting the ik customer i. B. mathematical formulation Now, the problem can be mathematically formulated as follows : f min( 1 f12 f 2 3 f 3) (1) f 1 K N N k1 i0 j0 K N K X ijk i1 k1 N K X ijk j1 k1 N i0 N N d ij X v ijk 1 j 2... N 1 i 2... N X 1 X 1 i0k N j0 0 jk k K N N X iuk X ujk i0 j0 jk Q k k K, u N y j N, k K y ( y q ) X jk D 0 0 D j ik j i j ijk [max( A, e )] s j i, j N, k K k K i, j N Constraints (2) and (3) restrict the assignment of each customer to exactly one vehicle route. Constraints (4) and (5) concerns the avalability of vehicles. Constraint (6) implies that the number of vehicles which have left the depot is equal to the number of vehicles coming back to the depot.constraints (7),(8),(9) and (10) ensure the schedule feasibility with respect to time considerations and capacity constraints. IV. HYBRID APPROCH TO SOLVE THE M-VRPTW The current trend is to use so-called hybrid algorithms, because it was observed that they allowed to obtain the better results. There are several possible types of hybridizations, in our case the genetic algorithm method and the variable neighborhood search method (VNS) were hybridized in order to improve the solutions obtained. The advantage is that local search explore efficiently promising areas, and an evolutionary method is excellent for detecting good regions in the search space. A. Genetic algorithm for the m-vrptw The first step of this hybrid approach finds the best solution by the genetic algorithm. 1) Initial population: Similar to most GA that a chromosome S is a permutation of n positive integers, such that each integer is corresponding to a customer. For example, a chromosome with 7 customers is S = (1, 2, 3, 4, 5, 6, 7), 0 is the depot, and according to the capacity of the vehicles, this chromosome may be broken into: f 2 max( 0, e j Aj ) X ijk Vehicle1 : k1 i0 j0 Vehicle2 : K N N Vehicle3 : f 3 max( 0, Aj l j ) X ijk k1 i0 j0 The The initial population is is generated generated randomly, randomly, also also by by using using a greedy greedy method, method, which which is is to to start start with with one one client, client, and and to 1 systematically to systematically move move to the to the nearest nearest client client that that has has not yet not been yet X If there is travel from i to j ijk visited. been visited. 0 Otherwise 2) Crossover :There are several different types of crossover :Crossover point,linear Order Crossover (LOX), Order Crossover(OX). Our approach to resolve this problem uses order crossover which can be explained as follow: First, two chromosomes are randomly selected from the initial population. Two cutting sites i and j are randomly selected in P1. Then,the substring P1(i)...P1(j) is copied into C(i)...C(j). Finally, P2 is swept circularly from j + 1 onward to complete C with the missing nodes. C is also filled circularly from j + 1.

28 The other child maybe obtained by exchanging the roles of P1 and P2.Table I gives an example demonstrating this process. TABLE I. EXAMPLE OF CROSSOVER OX Customers i and j 3 5 P P C B. Implementation of VNS in the hybrid approch The second step uses the variable neighborhood search to be close to the optimal solution. VNS is a metaheuristic for solving combinatorial and global optimization problems proposed by Hansen and Mladenovic [8]. The description consists of the building of an initial solution, the shaking phase, the local search method, and the acceptance decision. The initial solution is the best obtained by the genetic algorithm. In this study, the following two neighborhood structures are employed: Insert: It defines the closest neighborhood considered. This structure performs an insertion of a control point chosen randomly from the permutation, in front of another randomly chosen control point. Exchange: It is the other neighborhood structure used to explore new solutions in a little further vicinity of a solution. In this structure, two randomly selected control points are simply swapped. Local Search: this paper selects 2 opt as a local search operator in order to obtain the good quality local optimal solution in a short period. C. Dynamic case We consider that the dynamic event is the appearance of a dynamic client. New customer must be inserted into a single tour without modification of order of the unvisited clients and with a minimum of delays. The steps of the insertion method are: The planning of the routes to serve a set of clients, this is by applying the hybrid method (Previously presented). Determination of vehicle s position at the time of the appearance of dynamic client. Introduce the possibilities of insertion of dynamic demand, but clients in the previous appearance of this customer should be frozen. Update the routing of the vehicles, after the insertion which minimizes our objective function has been applied. D. Simulation and Results We tested our program using some instances from a randomly generated data on this site: (http://w.cba.neu.edu/~msolomon/problems.htm) The results that illustrate the distance, the average of delay time en min(s) and the average of stopping time en min(d) are shown in the following table (Table II): TABLE II. RESULT OF SIMULATION Best Known Solutions Results obtained by the proposed approach Identified by Heuristics problem NV Distance NV Distance D S R R R R R R NV: Number of vehicles CONCLUSION After a brief presentation of the state of the art in VRPTW, we proposed an approach based on genetic algorithms to minimize the weighted sum of the delays, stops and the total transportation time. The initial population are generated randomly, and also by using a greedy method, then improved through genetic operators. To improve the solution obtained by the application of GA, we opted for hybridization of VNS and GA. References [1] O. Br aysa, W. Dullaert and M. Gendreau, Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows, J. of Heuristics, 10 (2004), [2] O. Br aysa and M. Gendreau, Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms, Transportation Science, 39 (2005), [3] O. Br aysa and M. Gendreau, Vehicle Routing Problem with Time Windows, Part I :Metaheuristics, Transportation Science, 39 (2005), [4] G. B. Dantzig and R. H. Ramser, The Truck Dispatching Problem, Management Science, 6 (1959), nce, Vol. 6, No. 1 (Oct., 1959), pp Published by: INFORMS [5] M. Desrochers, J. K. Lenstra, M. W. S. Savelsbergh and F. Soumis, Vehicle Routing with Time Windows: Optimization and Approxinmation, In B. Golden and A. Assad (eds.) Vehicle Routing: Methods and Studies, Amsterdam: Elsevier Science Publishers, [6] B. L. Golden and A. A. Assad, Perspectives on Vehicle Routing: Exciting New Developments,Operations Research, 34 (1986), [7] B. L. Golden and A. A. Assad, Vehicle Routing: Methods and Studies, Amsterdam: Elsevier Science Publishers, [8] Hansen and N. Mladenović, Variable neighborhood search for the p- median, Location Science, vol. 5, no. 4, pp , [9] G. Ioannou, M. Kritikos and G. Prastacos, A Greedy Look-Ahead Heuristic for the Vehicle Routing Problem with Time Windows, J. of Operational Research Society, 52 (2001), [10] M. M. Soloman and J. Desrosiers,, Time Window Constrained Routing and Scheduling problems, Transportation Science, 22 (1988), 1 13.

30 The influence of HR practices on supply chain Asmaa Elmortada, Ahmed Mokhlis, Said Elfezazi Research Team in Industrial Engineering, Higher School of Technology of Safi city University Cadi Ayyad Marrakesh, Morocco Abstract In an economic environment increasingly complex and competitive, Moroccan enterprises, including industrial activity, are now aware, more than ever, of the importance of controlling the human resources function as a primary factor to the success and performance of their supply chains. The concern of this article is to highlight the role of HR function in the success of the supply chain, in the light of the culture of the company, and under the effect of HRM practices influenced by the competitive environment, and the resulting mutations. Keywords Human resources management, Morocco, Evolution of HRM, logistics chain, the company culture, relational strategy, transactional strategy... I. INTRODUCTION The logistic function becomes more and more a key role in acquiring a strategic importance for any business structure whose performance, and even the survival depend on the mastery of logistics processes implemented. This importance remains strategic and irreversible for the company to counter the systematic effects of the financial crisis that crosses all over the world and which weigh heavily on their social performance. This context highlights the needed responsiveness of organizational forms including productive nature mobilizing and combining diverse skills. This organizational work required depends on the ability of the company to attract and engage a competent and motivated human capital. Good human resource management as still an undeniable source of value creation that places humans and their creative potential at the center of the managerial and organizational concerns of a business. II. INFLUENCE OF HUMAN RESOURCE PRACTICES ON SUPPLY CHAIN A. The relationship between logistics and HRM Having been defined in several ways over the last 40 years, the called modern logistics is understood today as a function of planning, implementation and mastery of flows and stocks in the company. It relies on the implementation of information and communication systems increasingly sophisticated and takes place in the supply chain defined by Mentzer as "a set of three or more entities (companies or individuals) through which flow upstream and downstream products of services, information and finance, from a supplier to a customer." In terms of purpose, NFX standard specifies that the purpose of the logistic function is to "meet the needs expressed or latent, internal or external, to better economic conditions for a specific level of service." This definition makes that the notion supply chain can accommodate different realities as management practices flow may differ from one chain to another, according to the way that the relationship in the supply chain is oriented: Relational: Characterized by content (beginning, signals, end) of the communication between the partners in the chain and interactions mode (speed and frequency), allowing enterprising sides to get goals which are unattainable individually. Or transactional: In this relationship, the terms of exchange between buyers and suppliers must be specific (low flexibility of suppliers). The benefits and costs of each member of the supply chain are clearly defined (low ease of provider); little information flow between the parties (poor communication); members of the supply chain have little control over each other (low control); transactions are short-lived and have little chance to train new (sustainability little intended). According to the vocabulary of sale, each member is satisfied with a result of "win-lose" provided that they are winning. Therefore, the guidelines of the supply chain must be adapted to the culture of the company that comes into mutual influence with HR policies. Indeed, decisions on human resource management have a significant impact because if staff adapts to the culture of the company, it helps to highlight social knowledge, as it helps promote cohesion between the objectives of the employee and those of the company. "Sharing a social conscience helps employees to make the right decisions in the face of changing circumstances (Weitz and Jap 1995)." B. HR Strategies and Logistics As with relationships within the supply chain, human resources policies and culture derived from them may also be defined in

31 terms of transaction-relationship continuum. In other words, there are the relationship oriented HRM strategy that emphasizes the mutual loyalty between employees and the company, and that leads to a mutually beneficial relationship in the long term. There are also transaction-oriented human resource strategy in which employees are interchangeable and replaceable so that less effort is made to maintain their loyalty. By combining the two logistics strategies with both human resources strategies that a company can adopt, we find that two of the four possible combinations are compatible with the logistical objectives and HRM ("relational logistic-relational HRM "or "logistic transactional-hrm transaction ") while the other two combinations represent a strategic conflict: 1) combined relational strategies: Having adopted such a strategy, the objective of the company is to establish a longterm cooperation with most of its partners (customers, suppliers and distributors) and its own employees. A company oriented relationship tries to make the objectives of suppliers and distributors, in human relations and marketing, consistent and complementary, thus it treats customers, suppliers and distributors in the same way to obtain a strong cultural coherence promoting mutual confidence between the different parts enterprising (company, employee and various members of the supply chain). As part of a combined relationship strategy, employees are treated as investments where the importance of shared responsibility and factor "employee" domains of human resource management (lifetime employment, further education, higher salaries...). 1) Transactional Strategy: A company may also adopt a transactional approach with some or all members of the supply chain and its own employees. In this configuration the employee loyalty to the organization and the members of his chain is little considered. So we observe in the same way, a low degree of fidelity between the company and its suppliers and distributors. In this scenario, the employee evaluation is based on the volume of work without care of quality, and that's why employees are strictly defined and routine with minimal qualifications, which leads to situations where employees are replaceable and low paid, as it can also lead to a further reduction in the effective delivery of the service concerned with each personnel change. 2) Mixed strategy: long-term relationship with the members of the chain and transaction with employees: In this situation, the supply chain strategy is oriented relationship while human resources policy is transactional. The members of the supply chain then might receive a service of transaction level. This is because, firstly, the HRM strategy which closely defines the required work prevents staff to provide a comprehensive service and long term, especially as employees are not rewarded for the time dedicated to care of long-term relationships because the reward system is linked to quantitative and transactional goals. Secondly, the HR strategy is based on simple and routine jobs that require unskilled labor; workers are less able to provide the level of service required in a true relational environment. 3) Mixed strategy: short-term transaction with members of the supply chain and employee relations: In this case, the company may consider that the members of the chain are interchangeable, but she tries to get by against its employees a long-term commitment. Although maintaining a workforce of first class (skilled employees, following further training and receiving high salaries...) to perform transactional tasks is one of the few economic aspects of this approach, but it can be able to perform tasks that a member or chain transaction is unable or unwilling to perform. III. CONCLUSION Involved in achieving the basic objectives of internal and external coordination of physical and information flows, responsiveness to hazards, Primate of logistical requirements, HRM, in a mutual influence with the culture of the company, actively contributes in the determination of practices within the supply chain, both operational and strategic perspective. REFERENCES SEMMAE Mohamed, "Mobilizing and retain human resources in a context of crisis, which impacts on the future of HRM in the Moroccan industrial sector: the case of SOMACA, «Mobiliser et fidéliser la ressource humaine dans un contexte de crise, quels impacts sur le devenir de la GRH dans le secteur industriel marocain : cas de la SOMACA», viewed on accessed 01/27/2014 at 00: 35. MORENO Maxime, "Human Resource Management"«Gestion des Ressources Humaines», viewed on accessed 01/27/2014 at 00: 35. Mohamed Hicham BAAYOUD and Zouanat, "EVOLUTION OF THE FUNCTION IN MOROCCO HUMAN RESOURCES"/«EVOLUTION DE LA FONCTION RESSOURCES HUMAINES AU MAROC», viewed ysmediterranee/31-evolutionmaroc, accessed on 27/01/2014 at 00 :42. R. Bruce McAFEE, Myron GLASSMAN and Earl D. Honeycutt, Jr., "Influence of corporate culture and human resources policies on supply chain management strategy (SCM),"/«Influence de la culture d entreprise et des politiques de ressources humaines sur la stratégie Supply Chain Management (SCM)», viewed onhttp://www.logistiquemanagement.com/document/pdf/article/11_1_1702.pdf, accessed on 27/01/2014 at 00 :52. Thierry Jouenne, "The four levers of sustainable logistics,"/ «Les quatre leviers de la logistique durable», viewed on, accessed on 27/01/2014 at 00 :57.

34 Survey of vehicle fleet maintenance optimization: past and future trends EL KFITA Abdelaziz, Department of industrial Engineering, Ecole Mohammadia d Ingénieurs EMI Rabat, Morocco DRISSI-KAITOUNI Omar Department of industrial Engineering, Ecole Mohammadia d Ingénieurs EMI Rabat, Morocco Abstract Vehicle Fleet Maintenance management represents an important activity at tactical and operational levels to be faced by managers of fleet within private companies and public agencies devoted to passengers and freight transportation services, given the importance of fleet availability and reliability to keep a good operation and branding of the company or agency via its service. Mathematical models, computation techniques, operational research tools and technology have been developed for optimizing the maintenance cost of fleets in order to serve the customers demand with the objective of cost efficiency. The aim of this paper is to identify the most relevant problems in fleet maintenance management relative to transit and freight transport modes, and to present an overview of past and recent emerging trends. Keywords Vehicle Fleet Maintenance; Mathematical models; Operational research tools; Automotive telemetric. I. INTRODUCTION: Today the price of fuel continues to rise and threatening competitiveness between companies in the field of personal and goods transport, vehicles fleet maintenance optimization is considered by the managers of fleet as an important pillar to provide a cost effective and competitiveness of the enterprise service. In this sense vehicle maintenance represents a logistic support for the transport process, which on the other hand should provide for the transport service in order to satisfy client requests. Vehicle fleet maintenance objective is to have a vehicles in the state ready for operation in the exact moment when and the entire period during which they are needed with certain level of reliability. The evolution of fleet maintenance and management policies highlights the growing importance of maintenance issues in both private and public companies. The need to improve maintenance performance requires an accurate evaluation of the trade-off between costs and benefits related to alternative fleet maintenance and management policies. In the following of this paper we illustrate the consisting work since the 60s till now and highlight the trend of the research in this area. II. CONSISTING APPROACH OF VEHICLE FLEET MAINTENANCE MANAGEMENT The first works done in this direction were some attempts to apply classical methods to determine optimal replacement policies of a vehicle. The economic life approach which consists in replacing a vehicle after a fixed interval of time was applied widely at the beginning.but this approach is not very effective since it doesn't take into account the specificity of each vehicle [1]. In the early 70s the British Army used repair limit method which consists in comparing the eventual repair cost of a failed unit upon failure with a Repair Limit. If the estimated cost is less than the limit, the repair is carried out; otherwise a replacement is made [2]. However the Annual Maintenance Cost Limit approach used to set replacement decisions. Each year the decision to replace or not a vehicle is made by comparing the estimated maintenance bill for the next year with the AMCL. Besides, the authors showed how to take into account the effect of allowances on replacement decisions [3]. But once again, the suggested policy only set when it is preferable to replace than continue to maintain a vehicle, no schedule of the preventive maintenance is considered. Interesting work has been done in the railway sector; it is the maintenance of tramcars for Hong Kong Trams-way Company. In this case study, the vehicle is subject to regular overhauls and failures that were undertaken the general maintenance policy making the best use of opportunities provided by failed components and essential overhauls. The difficulty faced here is that the cost of a component preventive replacement depends on what else is being repaired at that time. The optimum age limits will not be constant but will depend on the age of the non-failed components. Therefore they proposed two suboptimal policies which are pair-wise control policies [4]. In other situation, elaborating a maintenance model for fleet of forklift trucks in an unsteady economy during a period of inflation and uncertain economy is difficult, and can t be effective only to take into account all these con-

35 siderations [5]. However, using a computer software package to follow-up maintenance planning for transit vehicles presents the advantage of exhibiting a real schedule of maintenance [6]. In fact the different components of a vehicle must followed with a accurate preventive maintenance and each component failure time can be modeled with a Weibull distribution and determined for each of them the best replacement mileage [7]. A. Preventive Maintenance of Vehicle Fleet. In this following section, the attention was brought to the importance of maintenance schedules and the determination of optimal inspection and preventive maintenance. Using the concept of delay-time analysis and recommending snap-shot modeling to define the optimal frequency of preventive actions of each vehicle, to reduce production downtime [8], in addition, all vehicles breakdowns can be influenced by the inspection frequency, thus the mean distance to failure varies with the value of the periodic inspection distance (every N- kilometers) and it s necessary to define an optimal inspection schedule which maximizes the vehicle availability [9]. Other works have been developed which dealt with the transit vehicles component maintenance applying Multiple Criteria Decision Making (MCDM) in order to take greater account when determining optimal policies, of the different criteria such as minimum cost rate, maximum availability and bottom-line component reliability [10]. However, the lack of available maintenance records and operating data rendered the study more difficult, this case require the use of subjective methods to both define the problem and to estimate parameters, in order to contribute both directly and indirectly to a change in work culture and to a reduction in vehicles breakdown rate [11]. On the other hand working on human resources is also very significant and important for decision making; it's about finding the optimal number of technicians and artisans that have a high productivity on the workshop to employ to carry out routine checks on vehicles on a waiting line [12]. B. Transit Bus Maintenance Constraints. This field has an increasing interest and the development of new organizational models for the management of freight movements within the city. In USA the nature of problems in transit bus maintenance during the years of 1985 is mostly institutional, rather than a question of hardware or resources [13]. In fact other components include in transit bus maintenance process such as network route design, setting timetables, scheduling vehicles, and assignment of drivers which requires mathematical formulation undertaken all this components for optimal maintenance of transit bus and optimal use of facilities [14]. In addition, the new approach using multiagent systems has been used to solve the bus maintenance scheduling which is considered a hard problem, knowing that is distributed and dynamic in nature [15]. C. Evalution of Fleet Maintenance. The evolution of fleet maintenance and management policies accentuate the increasing importance of maintenance issues in both private and public companies. The need to improve maintenance performance requires an accurate evaluation of the trade-off between costs and benefits related to alternative fleet maintenance and management policies. More often fleet managers are between two important following choices: 1. To repair or to renew the company fleet; 2. To increase investments in new bus or to expand the maintenance activities. Then to evaluate fleet maintenance management strategies a System Dynamics Model approach can effectively support fleet managers in designing and evaluating their strategies. Through such tool decision makers can test different fleet strategies and assess their effects on company performance [16]. However we want to exhibit the case of Logistic Service Provider (LSP) that has a great transportation fleet with many sub-companies, the fleet maintenance scheduling is considered a hard problem, to solve it; a mathematical model is presented as in [17], to determine the economic maintenance frequency of only a single company, thus the managers coordinate it among branches (sub-companies). There numerical results show that the whole transportation fleet system of an LSP can obtain significant cost savings from the coordination policy. Today companies are constrained by two importants factors: increasing diesel price and the environment pollution. For evaluating the fleet energy efficiency, it is indispensable to observe maintenance process that has a close relation with transport process and the environment, a hybrid tools of operational research in branch of multi criteria decision making (MCDM) are used, it s Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). In order to analyses the influence of indicators in all three mentioned areas on management decision-making. In this sense, appropriate indicators have been defined and subsequently used in fleet maintenance management. The proposed model has been implemented in several companies with road vehicle fleets. Collected results show the perceived evaluation by company managers in view of maintenance management process influence onto their fleet energy efficiency [18]. III. CONCLUSION AND PERSPECTIVES Many analytical approaches from Operations Research and Management Science are well used to handle the fleet maintenance management problems. These include modeling and simulation, Repair Limit Method, the optimal timing of inspection and /or N-mileage optimal frequency for preventive maintenance and the optimal number of technicians, Annual Maintenance Cost Limit approach, snap-shot modeling, delay-time analysis concept and Multi-Agent System (MAS), in addition, we discussed the application of hybrid Multiple Criteria Decision Making approach ( MCDM), it s the Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology combined with Analytical Network Process (ANP) all of these methods and approaches proposed to solve the problem of evaluation, and optimization of vehicles fleet maintenance.

38 Solving the fixed charge transportation problems using genetic algorithms and priority-based encoding Ahmed LAHJOUJI EL IDRISSI Department of Mathematics Faculty of science, Moulay-Ismail University, Meknes, Morocco Chakir TAJANI Polidisciplinary Faculty, Larache, Morocco Morocco Abstract In this work, we focus on the fixed charge transportation problem (fctp) which is an extension of classical transportation problem (TP) [1], where a fixed cost regardless of the quantity transported is added to the variable cost in order to find combination of routes that minimizes the objective which contains the total variable and fixed costs while satisfying the requirements of each origin and destination (supply and demand). Keywords Genetic algorithm; fixed charge transportation problem; priority based encoding; I. INTRODUCTION Transport problems are linear programming problems combining the producers and consumers; and operational research allows modelling of these problems using several methods with the aim to minimize the cost. The fixed charge transportation problem (fctp), is a type of the transportation problem. It contains two costs : variable cost proportional to the amount shipped and fixed cost regardless of the quantity transported. The objective is to minimize the total cost function, which can carry flows from the starting point to the end within the constraints. Balinski (1961) worked on the modification of problem fctp (reformulated as a linear integer program) [2]. He observed that there is an optimal solution for the modified version fctp, so it can be solved as a problem Classic. II. PROBLEM DESCRIPTION The Fund is classified as NP-complete problems that are difficult to solve with exact methods [3], because of the fixed costs that result in discontinuities in the objective function and the computing time needed to find a solution that risk increase exponentially with the size of the problem. Therefore, approximate methods and meta-heuristics should be exploited. This problem can be formulated as follows : Min Z j1 i1 subject to : i1 j1 x y y m n m ij ij ij i x x ij n n ij i1 a, b j, c ( c a b a i ij ij i j x, f j1 ij ij m f b j 0 ij y ij ) i 1, 2,...,m j 1, 2,...,n i 1, 2,...,m et j 1, 2,...,n si si x x ij ij 0 0 (1) (2) (3) It is better to consider the balanced problems. Indeed, it is easy to find a solution for these problems. The fctp requires the following parameters : i : index for source, (i = 1,2,..., m) j: index for depot, (j = 1,2,..., n) m: number of sources; n: number of depots; a i: amount of supply at source i; b j: amount of demand at depot j; c ij: variable transportation cost from source i to depot j; d ij: fixed transportation cost associated with route (i, j); The decision variables are: x ij: unknown quantity to be transported from source i to depot j; y ij: a binary variable which is 1 if xij > 0;

39 III. GENETIC ALGORITHM FOR THE FCTP A. Representation the FCTP (priority-based encoding) Gen et al. [4] developed a new method, called prioritybased GA, and proposed a new crossover operator; they used the position of a gene to represent a node (source/depot) and the value of a gene to represent the priority of the node for constructing a transportation tree among candidates. They developed encoding and decoding procedures using the transportation cost matrix. OPEX crossover After determining a random cut-point, the offsprings are generated using left segment of the cut-point and exchanging order of priority of nodes on the right segment between parents. Fig. 2. present a pseudo code of the OPEX. Fig. 2. OPEX crossover OPEX mutation Mutation is usually used to prevent premature lost of information. It is done by exchanging the information within a chromosome. We use the OPEX operator; however, instead of using this operator between two parents, we use it between two segments of a parent. Fig. 3 present a pseudo code of the OPEX mutation. Fig. 1. Priority-based encoding the TP By applying the priority-based encoding for a TP, a chromosome consists of priorities of sources and depots to obtain a transportation tree. Its length is equal to the total number of sources (m) and depots (n), m + n. To obtain the priority-based encoding for any transportation tree, a priority assignment to nodes is started from the highest value (m + n) and one until assigning priority to all nodes reduces it. In the fctp, we use another form of UC ij of the decoding algorithm in order to incorporate the special structure of the fctp. We apply the following equation to calculate the unit delivery cost of link (i, j): UCij= c ij 2 f ij min (a, b ) i j B. Initialization : The initialization of a chromosome is performed with randomly generating a permutation of digits from 1 to m + n. A priority assignment to nodes is started from the highest value (m + n) and reduced by one until assigning priority to all nodes. C. Genetic operators After determining a random cut-point, the offsprings are generated using left segment of the cut-point and exchanging order of priority of nodes on the right segment between parents. Fig. 3. OPEX mutation D. Evaluation and selection : Evaluation aims to attach a fitness value to each individual. The higher fitness value of an individual, the higher its chance for survival to the next generation. Therefore, evaluation plays a very important role in the evolutionary process. We use the inverse of the objective function as the fitness value during the decoding of a chromosome. In fact, the next generation is selected from a pool of parents and offsprings belong to the current generation. Then the best solution of the current population is substituted with one randomly selected solution of new population.

40 CONCLUSION FCTP is a nonlinear and difficult to solve the optimization problem, approximate algorithms and meta-heuristics should be exploited. GAs offer the optimal solution with a reasonable time, but to use the AG must choose an adequate representation to the problem addressed. Our vision is to improve the performance of algorithms using as representation priority-based encoding also develop new variants of operators used in GAs (crossing, mutation). References [1] F.L. Hitchcock, The distribution of a product from several sources to numerous localities, Journal of Mathematical Physics 20 (1941) [2] M.L. Balinski, Fixed cost transportation problems, Naval Research Logistic Quarterly 8 (1961) [3] M. Gen, R. Cheng, Genetic Algorithms and Engineering Optimization, second [4] M. Gen, F. Altiparmak, L. Lin, A genetic algorithm for two-stage transportation problem using priority-based encoding, OR Spectrum 28 (2006)

42 Reducing Fuel Consumption in Pickup Vehicle Routing Problem with Genetic Algorithm TAHA Anass Faculté des Sciences Agadir, Maroc HACHIMI Mohamed Faculté des Sciences Juridiques, Economiques et Sociales Agadir, Maroc MOUDDEN Ali Faculté des Sciences Agadir, Maroc Abstract The Vehicle Routing Problem is one of the most challenging combinatorial optimization task. The interest in this problem is motivated by its considerable difficulty as well as by its practical application in logistics. In this paper we propose a mathematical model to formulate pickup vehicle routing problem with a new objective function of reducing the total fuel consumption. Then, we develop a genetic algorithm to solve this problem. The experiments of this work show that the proposed genetic algorithm produce encouraging results. Keywords Pickup VRP, fuel Consumption, Genetic Algorithm, Transportation I. INTRODUCTION The Vehicle Routing Problem (VRP) [1] is one of the most challenging combinatorial optimization task. Defined more than 40 years ago, this problem aims at planning the routes of a fleet of vehicles on a given network to serve a set of clients under side constraints. The interest in VRP is motivated by its practical relevance as well as by its considerable difficulty. The literature on the VRP and its variants is rich [2], [3], A common variant is the capacitated VRP (CVRP) where the minimum-cost routes is found starting from and returning to the same depot that visits a set of clients once, each route may not exceed the capacity of the vehicle which serves that route. Almost all the research papers of VRPs focus on minimizing the total distance traveled by all vehicles or minimizing the total cost, which usually is a linear function of distance. However, vehicle managers are also interested in the vehicle routing schedules which minimize fuel consumption caused by two factors. First, reducing fuel consumption means reducing the service cost. Second, reducing fuel consumption means reducing its impact on our environment (green-house effect), our health (air pollutants), and our economy (increased fuel prices). Some related studies have taken into account energy consumption in vehicle routing from their own different perspectives. Suzuki [4] developed an approach to the time-constrained, multiple-stop, truck routing problem that minimizes the fuel consumption and pollutants emissions. This paper will provide a new pickup vehicle routing problem based on minimizing fuel consumption. Then we propose a mathematical model for this problem and a genetic algorithm to solve it. II. PROBLEM FORMULATION The main goal of this problem is to accomplish the pickups with a minimum total fuel consumption while meeting the overall client demands. We know the amount of fuel consumption for each unit distance without load, and the addition amount of fuel consumption for each unit distance with unit load. Our problem can be formally described as follows: G = (N, E) is a complete graph; N = {0, 1,..., n} is the set of nodes, with node 0 is the central depot; E = {(i, j) i, j N, i j} is the set of arcs defined between each pair of nodes; V = {1,..., m} is the set of vehicles related to the depot 0, each vehicle has identical capacity and all feasible vehicle routes correspond to paths start from one depot and end at the same depot; d ij, i, j N, is the distance between node i and j; C is the capacity of the vehicle; q i, i N, is the demand of node i, we consider the demand of the depot q 0 = 0; Q k i, i N, V is the load on the vehicle k when arriving in the node i; Q k 0 = 0, k V, indicate that the vehicle exits the depot empty to load goods from customers; c 0 is the amount of fuel consumption for unit distance without load; c 1 is the addition amount of fuel consumption for unit distance with unit load; The decision variable x k ij = 1, if vehicle k drives from node i to node j and x k ij = 0 otherwise. The objective function is formulated as follows: min ( c 0 d 0j x k 0j k V j N\{0} + ) ( ) c 0 + c 1 Q k j d ij x k ij (1) i N\{0} j N

43 Constraints related to the problem are: q j x k ij C, k V (2) j N i N x k ji, j N, k V (3) x k ij = i N i N x k ij = 1, j N\{0} (4) k V i N x k 0i 0, k V (5) i N k V i,j N x k ij U 1, U N\{0} (6) Q k j x k ij = (Q k i + q i )x k ij, i N\{0}, j N. (7) The object function (1) is to find the vehicle routing schedule with the minimal fuel consumption. Constraints (2) ensure that the load of each vehicle cannot exceed its capacity. Equalities (3) tell us that if vehicle visits a node then must leave from the same node. Equalities (4) guarantee that every customer is served by one and only one vehicle. Inequalities (5) ensure that a vehicle can leave more than one time from the depot 0. Constraints (6) forbid routes that are not connected with depot 0. Constraints (7) ensure that, when x ij = 1, the load Q k j of vehicle k between node i and node j equals the load Q k i of vehicle k arriving at node i plus the demand q i of node i loading at node i. III. GENETIC ALGORITHM FOR MINIMIZING FUEL CONSUMPTION Genetic Algorithms (GA) were first proposed by John Holland [6] in the 1960s. The idea behind a GA is to model the natural evolution by using genetic inheritance together with Darwins theory. A population of individuals representing tentative solutions is maintained over many generations. New individuals are produced by combining members of the population via crossover and mutation operators, and these replace existing individuals with some policies. In this part, we will present our approach based on genetic algorithm to minimize fuel consumption. A. Chromosome encoding Encoding allows us to represent a solution under shaped chromosomes. A chromosome is a sequence (permutation) of nodes, which indicates the order in which a vehicle must visit all nodes. To solve our problem with Genetic Algorithm, we will represent each individuals by one chromosome, witch is a chain of genes, represented by customers and vehicles. Each vehicle gene represents a separator between two different routes in the chromosome, and a string of customer identifiers represents the sequence of pickups that must cover a vehicle during its route. in the figure 1 below we can see a representation of a chromosome with 10 customers and 3 vehicles. Each route begins and ends at the depot assigned to number 0, if we find in a solution two vehicles not separated by any costumer, we will understand that the route is empty and it will not necessary use all the vehicles available. Fig. 1. 1, 2, 3, 11, 4, 5, 6, 7, 8, 9, 12, 10 } {{ } } {{ } route1 route2 Chromosome encoding }{{} route3, 13 Decoding permits from each chromosome in the population, to obtain a solution indicating the route of each vehicle on the corresponding nodes. An example of the final solution is as follows: B. GA framework Route 1 : {0, 1, 2, 3, 0} Route 2 : {0, 4, 5, 6, 7, 8, 9, 0} Route 3 : {0, 10, 0} The general framework of the genetic algorithm for minimizing fuel consumption can be shown as follow: Algorithm 1 GA Begin Initialize population (Randomly generated) Fitness evaluation Repeat Selection(Tournament selection); Optimized crossover; Mutation(swap and inversion mutation); Fitness evaluation; Elitism replacement; Until the Stop Condition is satisfied Return the fitness solution found End IV. SIMULATION AND BENCHMARKING After the description of the mathematical model and the genetic algorithm, we evaluate its performance by considering a set of instances relative to the capacitated vehicle routing problem. The algorithm is coded in java language and executed on a laptop PC, equipped with Windows 8 system, a Intel Core i7 processor at a 2.40 GHz speed, a RAM of 8,00 Go. Our experimental study is about a set of instances Of different sizes. These instances are from (Christofides and Eilon) [5]. Table 1 shows the parameterization used in our GA. Population size 2 x number of customers Selection of parents Tournament Selection Crossover probability 0,8 Mutation probability 1/ number of customers Replacement probability 0,1 TABLE I. PARAMETERIZATION USED IN OUR GA

46 A multi-level Particle Swarm Optimization for Solving Network Deployment Problem of RFID readers Abdelkader Raghib, Badr Abou El Majd, Brahim Aghezzaf Department of Mathematics and computer science Hassan II University Casablanca, Morocco Abstract Radio frequency identification (RFID) systems have received increased attention from academia and practitioners. RFID facilitates data acquisition and storage; it can provide accurate and real-time data without human intervention. This work proposes a new approach with the aim to optimize the deployment of RFID readers. In this way, Particle Swarm Optimization algorithm (PSO) is proposed in order to minimize the total quantity of readers required to identify all tags in a given area. The simulation results show the effectiveness of the proposed approach. Keywords Particle Swarm Optimization; RFID; Optimization; Deployment; Full coverage. I. INTRODUCTION Radio Frequency Identification (RFID) is an automated data collection technology using radio-frequency waves to transfer data between a reader and a tag in order to identify track and do management of material flow. The following figure shows the basic components of RFID systems. communication process between the reader and the tag. This process depends on correlated factors: the position and orientation of RFID tags, the interference from the environment, the type of material to identify and infrastructure limitations. One factor that is under control is the location and orientation of RFID reader. Generally, due to the limited read range of readers, multiple readers are required to detect and monitor the tags in RFID networks. Therefore, there is a need to determine the optimal quantity and positions of RFID readers in the area of interest without losing efficiency in the communication process. This problem has studied by many researchers in order to cover all tags in the entire region with a few number of readers. Unlike the approaches developed in [1,2], which consist to place the readers only in candidate fixed positions, we propose an efficient approach, based on PSO with several attractive features: determining the minimum number of readers, finding their optimal positions without fixed them, and guarantying a full coverage of all the tags. Fig. 2, example of RFID deployment. Fig. 1, Basic components of a typical RFID system. An electronic tag, attached to an item, is scanned by a reader that can be mobile (hand-held), and can also be at a fixed point such as an entrance/exit, or as a point of sale. The success in the identification of items depends on the II. METHODS In this work, the deployment of RFID readers concerns three principals objectives that are formulated as follow: Guarantying a full coverage.

47 Minimizing the number of readers and finding their optimal positions. Minimizing the interference. In order to find an optimal deployment of the RFID readers, the PSO algorithm [3] has adopted for this investigation due to its simplicity, efficiency, robustness, flexibility, ease of application and implementation. This advantage makes PSO algorithm more robust than many search algorithms, and has been successfully applied in many areas and solved a variety of optimization problems in a faster and cheaper way [4-8]. In this work, we developed and compared three methods: the first one starts by deploying randomly a reader, and then updates its position in order to determine the best location required to cover the maximum of tags. At each update, the best position is searched until we reach the full coverage, or we increment the number of readers when a search timeout occurs. In the second one, we save all the previous best positions when a search timeout occurs, and we start to update only the new reader until we find a full coverage. It s faster in term of CPU time but less accurate than the first one. The third method, illustrated by the figure 2, addresses the previous limitation by combining the first and the second method to yield even higher accuracy and efficiency. A key step in this algorithm is checking the best location of the readers in the proximity of the best previous positions when the search timeout occurs. III. RESULTS AND DISCUSSION This experiment was conducted to validate the proposed approach and evaluate the performance of the both methods. In this case, we considered 200 tags distributed randomly in a 10*8 m rectangular space, the read range of each reader is 3m and the maximum iterations for each PSO algorithm is set as The following figure shows the simulation results: Fig. 4, Compare the results of the three methods. Clearly, each method of the proposed approach reaches a full coverage by using a minimal number of readers, and finds the optimal positions tags in the search space. However, the difference in term of the number of iterations needed to reach a full coverage can be explained by the fact that the first method scan the search space more than the second one. Comparing the three results, we can deduce that the first method guarantees the quality of the solution, but needs more execution time than the second one. The third method combines the first and the second method to yield even higher accuracy and efficiency. IV. CONCLUSION The main goal of the current study was to find an optimal deployment of the RFID readers. The simulation results demonstrate the efficiency of the proposed approach by determining a minimal number of readers, finding their optimal positions and guaranteeing a full coverage of all tags in the working area. As perspectives, we propose to use the three previous methods in real applications. Moreover, we suggest solving the deployment problem by using the multi-objective optimization and the game theory. REFERENCES Fig. 3, Flowchart of the third method. [1] Hsing-Fang Tsai, Shin-Yeu Lin, Genetic Algorithm for Reader Network Planning Problem, UACEE International Journal of Advances in Computer Networks and its Security IJCNS, vol. 3, pp , June [2] Indrajit Bhattacharya, Uttam Kumar Roy, Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization, International Journal of Computer Networks & Communications IJCNS, vol.2, no.6, November 2010.

54 Supply Risk management in supply chains Multi-Agent System Approach Ourich Youssef Laboratory of System Analysis, Information Processing, and Integrated Management EST-Mohammed V University Rabat, Morocco Jamila EL ALAMI Laboratory of System Analysis, Information Processing, and Integrated Management EST-Mohammed V University Rabat, Morocco Abstract- Different models and approaches are used for risk management in supply chains and require simulation techniques taking into account the temporal reality of the processes and interactions among multiple actors. At first, this is how to integrate risk officer of a Multi-Agent System, capable of making decisions and building coalitions. Risk situations create imbalances in one of the links in the supply chain. More specifically, the process acting on dynamic discrete event systems. Taking into consideration specificities concerning the flow of transport and road traffic. We present an approach for the formation of coalitions between agents regardless of the nature of agents and tasks performed based on risk notifications. Keywords supply chain; multi-agent system; risk; coalition; transport flow; traffic flow. I. INTRODUCTION Modeling the supply chain is the subject of a growing body of theory involving more often autonomous entities constantly interacting in a dynamic environment. Several research studies have been conducted on this topic based on multi-agent systems (MAS) to model the coordination between the different actors. Similarly to the road network, a supply chain is described as a network of distribution station (HUB) interconnected by pathways routing products. Today, many approaches, methods use the term risk in supply chains, "Supply Chain Risk Management" (SCRM), however, a first definition of risk in the field of supply chain management, was given by March and Shapira (1987). They define risk as "The variation in the distribution of possible supply chain outcomes, their likelihood, and their subjective values ". A risk is a break in flow between components of the supply chain and subsequently, a future event. As regards to the paradigm of management of the supply chain, among the many definitions assigned, we will retain that of Simchi-Levi. They state Supply chain management is set of approaches Mohammed BILALI Laboratory of System Analysis, Information Processing, and Integrated Management EST-Mohammed V University Rabat, Morocco Aziz SOLHI École Nationale Supérieure des Mines de Rabat Mohammed V University Rabat, Morocco utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed in the right quantities, to the right locations, and at the right time, in order to minimize system wide costs while satisfying service level requirements... [Simshi-Levi et al, 2000]. This chain consists of a number of players linked with a large number of suppliers, customers, assembly center, etc. [Derrouiche et al, 2010]. An agent is a real or virtual entity, operating in an environment, able to perceive and act on it, which can communicate with other agents; it exhibits an autonomous or semi-autonomous behavior, which can be seen as the consequence of knowledge, its interactions with other agents and the goals it pursues. [Ferber, 1995], therefore, to achieve the same goals, we tend more often to build coalitions between different types of agents in order to make them cooperate in a multi-agent system [Sandholm, Larsen et al, 1998] under constraints that will be developed later. The aspects of collaboration constitute currently a challenging research field for the management of the supply chain called "collaborative". A well-known example is that of a road traffic, several studies have been completed in order to model the flow of traffic [Gartner et al, 1987]. Among those works, we cite the queuing theory, mainly interested in the process of starting and ending in specific parts of the network [Webster, 1958]. And the theory of the following vehicle, which studies how a vehicle reacts in relation to the movements of other vehicles on its way [Chandler et al., 1958] in order to adopt the preceded models, as well as, the interactions that encourage to form coalitions, to the multiagent systems. In this context, the literature contains several studies using Petri Nets [Tolba et al, 2010] for modeling and performance analysis of discrete event dynamic systems. Our contribution aims to develop a new communication protocol based on the type of coalition in a context of risk for road traffic as well as negotiations and possible coordination

55 with different types of agents forming the related process. It will also answer the following problem: what strategy to adopt in response to a notification of an external risk affecting the process by the agent risk regarding other agents that share the same process container? II. SUPPLY CHAIN AND MULTI-AGENT SYSTEM According to [Swaminathan et al.1998], A supply chain can be defined as a network of autonomous or semiautonomous business entities collectively responsible for procurement, manufacturing and distribution activities associated with one or more families of related products. We will add to this that the supply chain extends from raw material supplier to the final customer. In the other hand the term collaboration in the supply chain is built on many features as stated by [Min et al, 2005].The principal features of collaboration are information sharing, joint planning, joint problem solving, joint performance measurement, and leveraging resources and skills. In Multi- Agent System collaboration imply work and coordinate with other agents in a joint effort to realize benefits or to mitigate a risk. Thereby, it is important to identify the risks in order to manage and control them. This is possible through a good risk mapping and effective process management. The mastering of the process and their environment help, significantly, to identify, easily, the causes of the risk and the available resources. TABLE I. A RISK MANAGEMENTMODEL IN A LOGISTICS NETWORK (PROPOSED BY HARLAND ET AL. 2003) Stages Description 1 Network mapping 2 Risk identification and its current location in the network 3 Risk evaluation 4 Risk management Details Structural factors Important measures Possession Type Potential loss Probability of occurrence Step in the Life Cycle of Risk Risk exposure Likely triggers Probable losses Implementation of an attitude toward risk Scenario development 5 Development of a collaborative strategy in the network 6 Implementation and deployment of a collaborative strategy in the network The risk can be seen as a process in itself, or can be integrated in a process as an entity capable of interacting with other entities in the process with decision-making power, which varies depending on the situation surrounding the process. In Multi-Agent System, negotiation is the process by which agents solve their conflicts by coordinating their actions, sharing limited resources and arguing their points of view; in order to best satisfy their respective interests. Negotiation is one possible approach; other alternatives could be to be authoritarian in the process of decision-making. In the latter case, conflicts are not explained and the interests of agents may be affected. Under normal situations, the cooperative negotiation states that agents have a common collaborative goal, and cooperate by CNP protocol "Contract Net Protocol"» [Davis & Smith 81-83]. III. FLOW TRANSPORTATION We distinguish on one hand, direct risks those proper to transport or to the storage during merchandise handling etc., on the other hand, the indirect risk are those related to the additional costs of the previous risks and the exceptional risks that may occur during all phases of the supply chain. By projecting these risks on each process of the supply, chain in particular the transport, which is considered as a compound of agents communicating with each other and desiring to negotiate, cooperate and even make coalitions to achieve goals. The identification and reporting of risk is perceived as an external agent that can communicate with internal staff, and leading to the creation of a real or virtual agent as a trigger for coalition and decision regulation (encouraging other agents to coalitions during a presence of risk) knowing that the notification time is much less than the time to reach the vehicle identified as risky while traveling on road traffic. Several theories have developed negotiation protocols such is the case of game theory (concepts auction). Game theory deals with the development of contracts, distribution of profit and conflict resolution. This theory also uses strong notions of convergence based on the Nash equilibrium and Pareto optimality according to [Zlotkin, Rosenschein, 1994]. An extension was made by [Sandholm 1993] and enriched the CNP protocol by a price mechanism more precisely the agents locally calculate their marginal costs to complete sets of tasks and the choice of the contractor based on those costs. Our contribution is to overload these costs (weight concept) as a response to the strategy in the presence of a notification of risk originating from an external environment (the case of road traffic notification through connected PDA informing others about traffic congestion or accident, etc..) to force the coalition between agents in the presence of a risk. This will change the nature of egoist agent in collaborative one capable to forge coalition. The resulting of these coalitions named the "Risk Prediction Agent" RPA is an agent (having a maximum weight) with authority to make decisions to notify other agents inter or extra process to avoid disrupting the ordinance of other processes in the supply chain. The integration of risk in the multi-agent system takes into account the components of the collaborative logistic chain architecture [Thun et al, 2011] will be achieved through various modeling elements. In the case of the new agent s redefinition, the risk will be classified according to several ways [Saleh Ebrahimi et al, 2012], we will determine its scope, effect type and the affected agent. Taxonomy of risk would be necessary for agent integration in the context of collaborative logistic chain. Planning, organization and transport flows management [Mes et al, 2010], will be treated taking into account the risk identified by the multi-agent systems. Conclusion To extend this model to the coalition of agents between processes in the supply chain, we need to define and to use existing protocols already developed in this area. Therefore, in a context of dynamic tasks, it is clear to take into account in the

62 Selection of optimal supplier in supply chain using a multi-criteria decision making method Hanane ASSELLAOU, Brahim OUHBI 2MCI laboratory, ENSAM Meknes Meknes, Morocco Abstract The supplier selection problem is one of the strategic decisions that have a significant impact on the performance of the supply chain. This article presents a comprehensive methodology for supplier selection. The proposed methodology consists of Analytic Network Process (ANP), the criteria which are relevant in the supplier selection, have been used to construct an ANP model. The application of this method has been demonstrated through an illustrative example. Rejectability criteria Bouchra FRIKH LTTI laboratory, EST-Fès, Fes, Morocco All of the sub-criteria are given a code letter Keywords component; Supplier selection, ANP I. INTRODUCTION (HEADING 1) Since 1960s, supplier selection criteria and suppliers performance have been a focal point of many researchers. As a pioneer in supplier selection problem, Dickson (1966) identified 23 different criteria for this problem including quality, delivery, performance history, warranties, price, technical capability and financial position. Weber et al. (1991) analyzed 74 articles published between 1966 and 1990 dealing with this problem. Extensive multi-criteria decision making approaches have been proposed for supplier selection, such as the analytic hierarchy process (AHP), Analytic Network Process (ANP), Case-Based Reasoning (CBR), mathematical programming, Data Envelopment Analysis (DEA), Genetic Algorithm (GA) Many decision problems cannot be building ad hierarchical because of dependencies, influences between and within clusters (criteria, alternatives). ANP (saaty, 2001) is very useful to solve this kind of problems. II. THE PROPOSED MODEL IN SUPPLIER SELECTION: Assume that an automotive company is in a decision-making situation for purchasing one of the main items for their newly introduced product. A committee of three decision-makers {D1, D2, D3} wants to select the most promising vendor for supplying the item. After a preliminary screening, three alternatives {A1,A 2,A3} remain for further evaluations. The network structure of this problem is depicted in Figure 1. The main goal of the supplier selection is selecting the best supplier that meets the requirements or criteria. For the proposed supplier selection model, overall criteria are determined under the below mentioned three main criteria clusters: Selectivity criteria; III. APPLICATION OF SUPPLIER SELECTION: Step1: paire wise comparaison of clusters: In this step, a series of pairwise comparisons are made to establish the relative importance of clusters in achieving the objective. In such comparisons, a ratio scale of 1_9 is used to compare any two elements. A score of 1 indicates equal importance of the two elements whereas a score of 9 indicates overwhelming dominance of the elements under consideration (row component) over the comparison element (column component). The matrix showing the pairwise comparison of clusters along with the derived local priority vectors (also known as e-vectors (eigen vectors)) is shown in table 1..

63 Table 1: paire wise comparisons of clusters Selectivity criteria Rejectability criteria e-vectors Selectivity criteria Rejectability criteria 1/ Step 2: pairwise comparison of criteria In this step, the relative importance of each criterion for cluster l (selectivity criteria) is obtained through a pairwise comparison matrix. Two such matrices would be formed in the present case. One each for the two clusters. The matrix for selectivity criteria cluster is shown in table 2. Table2: pairwise comparison of criteria for cluster 1 Quality Delivery Flexibility e-vectors Quality Delivery 1/4 1 3 Flexibility 1/2 1/ THE MATRIX FOR REJECTABILITY CRITERIA CLUSTER IS SHOWN IN TABLE 3 Table3: pairwise comparison of criteria for cluster 2 Credit risk Price e-vectors Credit risk 1 1/ Price Step 3: pairwise comparaison of subcriteria: In this step, the pairwise comparaison of elements at each level is conducted with respect to their relative influence towards their control criterion. one such pairwise comparison matrix for quality under the selectivity criteria cluster is shown in table 4: Table4: The pairwise comparison matrix for quality under the selectivity criteria cluster QSC RQ RR e-vectors QSC 1 4 1/3 RQ 1/4 1 5 RR 3 1/ It is observed from table 4 that QSC has the maximum influence on quality under selectivity criteria Step 4: pairwise comparison for interdependencies: In this step, pairwise comparisons are made to capture interdependencies among the criteria. The pairwise comparison matrix and relative importance weight results for other criteria and impact on quality criterion is shown in table 5: Table5: The pairwise comparison matrix and relative importance weight results for other criteria and impact on quality criterion Quality Delivery Flexibility Credit risk Price W Delivery ,536 Flexibility 1/ ,218 Credit risk 1/4 1/ ,179 Price 1/6 1/3 1/5 1 0,064 Step 5: Evaluation of providers The final set of pairwise comparaison is made of the relative impact of each of the alternatives (provider 1, 2et 3) on the subcriteria in influencing the clusters. The number of such pairwise comparaison matrices is dependent on the number of subcriteria that are included in each cluster. In the present case 11 subcriteria for the selectivity criteria cluster and 6 subcriteria for the rejectability criteria, this leads to the formation of 17 such pairwise comparaison matrices. Matrix for alternatives impact on subcriterion (DR) in influencing the selectivity criteria: Table6: Matrix for alternatives impact on subcriterion (DR) in influencing the selectivity criteria Provider 1 Provider 2 Provider 3 Provider ,512 Provider 2 1/ ,36 Provider 3 1/3 1/4 1 0,128 Step6: super-matrix formation: After we have all the pair-wise comparisons completed, we go to our next step, to evaluate those criteria with interdependencies using super-matrix analysis. The super matrix as shown in table 7, presents the results of the criteria Table 7: super matrix before convergence Quality Delivery Flexibility Credit Price risk Quality 1 0,327 0,351 0,298 0,322 Delivery 0, ,2118 0,209 Flexibility 0, ,257 0,1812 0,224 Credit 0, ,052 risk 0,177 0,110 Price 0,064 0,233 0,221 0,223 1 These converged values turn out to be Wf= 0.240, 0.203, 0.165, 0.110, for quality, delivery, flexibility, credit risk, price Step7: selection of the best provider: Identify applicable sponsor/s here. If no sponsors, delete this text box (sponsors).

64 The selection of the best provider depends on the values of various desirability indices. The final step of the process is to aggregate the values to arrive at the final scores for each provider. This aggregation is a weighted average sum calculation defined by expression FDkl the direct (dependent) relative importance score of criterion k within a Cluster l. (e.g. a score for quality criterion which appears within the selectivity criteria cluster). FI kl the interdependent relative importance score of criterion k within the Cluster l as determined by the super-matrix results. 4) Levary, R. R. (2008). Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Computers and Industrial Engineering, 55(2), Provideri is the overall desirability index score for Provider i. Jk is the index for the number of selected subcriteria for a criterion k. Kl the index for the number of selected criteria for a Cluster l. 5) Narasimhan, R. (1983), An analytic approach to supplier selection. Journal of Purchasing and Supply Management, 1, ) Saaty, T. L. (2001). The Analytic Network Process: Decision Making with Dependence and Feedback, RWS Publication, Pittsburgh, PA Cl the relative importance score of a Cluster l at the top level (e.g. a score for the selectivity criteria cluster). SSjkl the relative importance score for a subcriterion j controlled by criterion k within Cluster l (e.g. a score of Reliability of quality under the quality criterion within the selectivity criterion cluster). Aijkl the relative importance score of an provider i for subcriterion j under criterion k within the Cluster l. Desirability index calculations for provider selection: References: 1) Dickson, G.W., An analysis of vendor selection systems and decisions, Journal of Purchasing, vol.2, pp ) Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems and Applications, 39(8), ) Jharkharia, S. and Shankar, R. (2007). Selection of logistics service provider: An Analytic Network Process (ANP) approach. Conclusion: Select suppliers are difficult given the qualitative and quantitative criteria. Since selecting the best supplier involves complex decision variables, it is considered to be a multi criteria decision problem. The ANP approach, as a methodology used to select the best supplier, not only leads to a logical result but also enables the decision- makers to visualize the impact of various criteria in the final result.

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